Detection of pine wood nematode infestation using hyperspectral drone images

Abstract

The pine wood nematode (PWN) is one of the most threatening pests of pine forests in China. An essential management response involves felling and removing the infested trees before the PWN can spread over large areas, but this requires early detection. In this study, we analyzed the temporal changes in the spectral signatures of healthy and infested trees throughout the entire growing season (May to October) using unmanned aerial vehicle (UAV) hyperspectral images (HSI). To explore the potential of these seasonal spectral changes in detecting PWN, we investigated the separability of healthy and infested trees using both absolute values and changes in values. A random forest (RF) classifier was applied to separate healthy and infested trees based on reflectances, derivatives, vegetation indices (VIs), and their seasonal change values. We obtained the following results: (1) the seasonal changes of tree crown spectra following a PWN infestation were similar to those observed using absolute spectral values, though the latter performed better in early detection; (2) in the early stage of infestation (June and July), the green and red-edge regions contained the most sensitive bands, whereas the near-infrared portion played a more important role in later stages; (3) derivatives achieved higher overall accuracy (OA) than the reflectance in the early stage of PWN infestation (June and July); (4) in June, the Pigment Specific Simple Ratio (PSSR) had the highest separability (OA = 0.77) for PWN infestation, followed by the Plant Senescence Reflectance Index (PSRI) and the Normalized Difference Red-edge Index (NDRE), both with a OA of 0.70. In July, PSRI, Red Edge Normalized Difference Vegetation Index (RENDVI), and the Modified Red Edge Simple Ratio (MRESR) had the highest OA of 0.77. These VIs were calculated based on the red-edge regions. In August, RENDVI achieved the best accuracy (0.87), followed by the Normalized Difference Vegetation Index (NDVI, OA = 0.83) and the Plant Stress Index (PSI, OA = 0.80). In September and October, PSRI, PSI, the Greenness Index (GI), and RENDVI showed the strongest separability (0.87 to 0.90). Similar results were obtained using change values of VIs. In June, RENDVI, PSI, and NDVI showed the highest accuracy (OA = 0.73). In July, MRESR had the best accuracy (0.77), followed by RENDVI (0.73) and PSI (0.73). In the late stage of PWN infestation, NDVI, PSRI, PSI, RENDVI, GI, and PSSR obtained the highest accuracies (0.83 to 0.93), surpassing the accuracy achieved using absolute spectra; (5) in RF classification model, seasonal variation values derived from reflectances, derivatives, and VIs performed well in the late stages of the PWN infestation (September and October) but were not as good as absolute values in the early and middle stages (June to August). Our study explored the potential of seasonal changes in UAV spectra for early detection of forest pests.